1 Intro

Mapping wind turbine acceptance model data output.

Based on:

2 Load LSOA boundaries and lookup

We need these for all maps.

## Loading LSOA boundaries from file
## 
## Basingstoke and Deane        East Hampshire             Eastleigh 
##                   109                    72                    77 
##               Fareham               Gosport                  Hart 
##                    73                    53                    57 
##                Havant         Isle of Wight            New Forest 
##                    78                    89                   114 
##            Portsmouth           Southampton           Test Valley 
##                   125                   148                    71 
##            Winchester 
##                    70

Build a simple map just to check (Figure 2.1.

## Coordinate Reference System:
##   User input: OSGB 1936 / British National Grid 
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##             ELLIPSOID["Airy 1830",6377563.396,299.3249646,
##                 LENGTHUNIT["metre",1]]],
##         PRIMEM["Greenwich",0,
##             ANGLEUNIT["degree",0.0174532925199433]],
##         ID["EPSG",4277]],
##     CONVERSION["British National Grid",
##         METHOD["Transverse Mercator",
##             ID["EPSG",9807]],
##         PARAMETER["Latitude of natural origin",49,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8801]],
##         PARAMETER["Longitude of natural origin",-2,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8802]],
##         PARAMETER["Scale factor at natural origin",0.9996012717,
##             SCALEUNIT["unity",1],
##             ID["EPSG",8805]],
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##             LENGTHUNIT["metre",1],
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##         PARAMETER["False northing",-100000,
##             LENGTHUNIT["metre",1],
##             ID["EPSG",8807]]],
##     CS[Cartesian,2],
##         AXIS["(E)",east,
##             ORDER[1],
##             LENGTHUNIT["metre",1]],
##         AXIS["(N)",north,
##             ORDER[2],
##             LENGTHUNIT["metre",1]],
##     USAGE[
##         SCOPE["Engineering survey, topographic mapping."],
##         AREA["United Kingdom (UK) - offshore to boundary of UKCS within 49°45'N to 61°N and 9°W to 2°E; onshore Great Britain (England, Wales and Scotland). Isle of Man onshore."],
##         BBOX[49.75,-9,61.01,2.01]],
##     ID["EPSG",27700]]

Figure 2.1: LSOA check map (shows MSOA and ward names when clicked

3 Mapping Wind Turbine Acceptance (LSOAs)

This data is the output of a model described in https://doi.org/10.1016/j.enpol.2019.01.002. A further paper used the model to compare the West Midlands and Solent regions: https://doi.org/10.1093/ijlct/ctz006. The latter noted:

“From a resource perspective, the Solent area is highly suitable with many hilly regions and its coastal location offering high wind speeds. However, the opportunity for development is limited by National Parks and Areas of Outstanding Natural Beauty (AONB), and the sites that are located outside of these regions are largely unsuitable for development due to the demographic composition.”

The model was based on a range of physical factors, modelled wind resource potential, known constraints and local socio-demographics (from Census 2011 data). ‘Acceptance’ refers to the likelihood of planning acceptance based on Harper et al’s model which was developed using a database of planning outcomes for wind turbines from across the UK.

The acceptance indices were originally estimated at 100m squares and have been aggregated to LSOAs (min, max and mean). Note that the model outputs used here do not take account of the current local built form - so they do not indicate feasibility, merely the probability of a successful planning application assuming there is land available on which to build turbines.

Figure 3.1 maps the acceptance probabilities for the Solent region at LSOA level.

Figure 3.1: Modelled wind turbin planning acceptance probabilities (LSOAs, Solent region)

As noted above, modelled wind turbine acceptance levels are low in most of the Solent region - generally due to physical/landscape constraints such as presence of two National Parks, AONBs etc. There are some interesting outliers - some built up (urban) areas have relatively high acceptance probabilities (up to 27 % ). Remembering that the model does not take account of available land (see above), these are most likely due to the socio-demographic factors which tended to increase the probability of acceptance in certain areas (e.g. areas with higher mean age and higher level of qualification have lower acceptance rates).

LSOA11CD LSOA11NM WD20NM RUC11 Group Name min mean max
E01022540 Basingstoke and Deane 007C Popley East Urban city and town Hampered neighbourhoods 0.1793486 0.2687852 0.2848143
E01022556 Basingstoke and Deane 001D Baughurst and Tadley North Urban city and town Comfortable neighbourhoods 0.1781918 0.2661691 0.2925525
E01022875 Hart 003E Blackwater and Hawley Urban city and town Households in terraces and flats 0.1760396 0.2634963 0.2753256
E01032851 Basingstoke and Deane 007J Popley East Urban city and town Hampered neighbourhoods 0.1064899 0.2590158 0.2994626
E01022539 Basingstoke and Deane 007B Popley East Urban city and town Hampered neighbourhoods 0.1079958 0.2576760 0.2848143
E01022555 Basingstoke and Deane 001C Tadley Central Urban city and town Aspiring urban households 0.1537842 0.2462379 0.2912621
E01022560 Basingstoke and Deane 001E Tadley South Urban city and town Prospering countryside life 0.1181101 0.2390197 0.2560756
E01022523 Basingstoke and Deane 008D Norden Urban city and town Hampered neighbourhoods 0.1125596 0.2378786 0.2724589
E01022524 Basingstoke and Deane 009A Norden Urban city and town Households in terraces and flats 0.1696027 0.2355656 0.2457919
E01022543 Basingstoke and Deane 007F Popley West Urban city and town Hampered neighbourhoods 0.1713527 0.2343527 0.2597786

4 Comparison with Domestic Electricity consumption (LSOAs)

Just for fun. As we saw, wind turbine acceptance levels are low - mostly due to physical constraints

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

As we would probably expect, those areas with highest turbine acceptance probabilities tend to have lowest total domestic electricity use.

For even more fun, here’s the plot split by OAC Group name

5 The end

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